Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancer
Breast cancer, a prevalent cancer among women worldwide, necessitates precise and prompt detection for successful treatment. While conventional histopathological examination is the benchmark, it is a lengthy process and prone to variations among different observers. Employing machine learning to aut...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-03-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2024.1373244/full |
| _version_ | 1797271485842194432 |
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| author | Moteeb Al Moteri T. R. Mahesh Arastu Thakur V. Vinoth Kumar Surbhi Bhatia Khan Surbhi Bhatia Khan Mohammed Alojail |
| author_facet | Moteeb Al Moteri T. R. Mahesh Arastu Thakur V. Vinoth Kumar Surbhi Bhatia Khan Surbhi Bhatia Khan Mohammed Alojail |
| author_sort | Moteeb Al Moteri |
| collection | DOAJ |
| description | Breast cancer, a prevalent cancer among women worldwide, necessitates precise and prompt detection for successful treatment. While conventional histopathological examination is the benchmark, it is a lengthy process and prone to variations among different observers. Employing machine learning to automate the diagnosis of breast cancer presents a viable option, striving to improve both precision and speed. Previous studies have primarily focused on applying various machine learning and deep learning models for the classification of breast cancer images. These methodologies leverage convolutional neural networks (CNNs) and other advanced algorithms to differentiate between benign and malignant tumors from histopathological images. Current models, despite their potential, encounter obstacles related to generalizability, computational performance, and managing datasets with imbalances. Additionally, a significant number of these models do not possess the requisite transparency and interpretability, which are vital for medical diagnostic purposes. To address these limitations, our study introduces an advanced machine learning model based on EfficientNetV2. This model incorporates state-of-the-art techniques in image processing and neural network architecture, aiming to improve accuracy, efficiency, and robustness in classification. We employed the EfficientNetV2 model, fine-tuned for the specific task of breast cancer image classification. Our model underwent rigorous training and validation using the BreakHis dataset, which includes diverse histopathological images. Advanced data preprocessing, augmentation techniques, and a cyclical learning rate strategy were implemented to enhance model performance. The introduced model exhibited remarkable efficacy, attaining an accuracy rate of 99.68%, balanced precision and recall as indicated by a significant F1 score, and a considerable Cohen’s Kappa value. These indicators highlight the model’s proficiency in correctly categorizing histopathological images, surpassing current techniques in reliability and effectiveness. The research emphasizes improved accessibility, catering to individuals with disabilities and the elderly. By enhancing visual representation and interpretability, the proposed approach aims to make strides in inclusive medical image interpretation, ensuring equitable access to diagnostic information. |
| first_indexed | 2024-03-07T14:03:23Z |
| format | Article |
| id | doaj.art-bf456ff5c1fb43b0b9a3fe849b3a6137 |
| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-03-07T14:03:23Z |
| publishDate | 2024-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj.art-bf456ff5c1fb43b0b9a3fe849b3a61372024-03-07T04:39:56ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2024-03-011110.3389/fmed.2024.13732441373244Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancerMoteeb Al Moteri0T. R. Mahesh1Arastu Thakur2V. Vinoth Kumar3Surbhi Bhatia Khan4Surbhi Bhatia Khan5Mohammed Alojail6Department of Management Information Systems, College of Business Administration, King Saud University, Riyadh, Saudi ArabiaDepartment of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, IndiaDepartment of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, IndiaSchool of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, IndiaDepartment of Data Science, School of Science Engineering and Environment, University of Salford, Manchester, United KingdomDepartment of Electrical and Computer Engineering, Lebanese American University, Byblos, LebanonDepartment of Management Information Systems, College of Business Administration, King Saud University, Riyadh, Saudi ArabiaBreast cancer, a prevalent cancer among women worldwide, necessitates precise and prompt detection for successful treatment. While conventional histopathological examination is the benchmark, it is a lengthy process and prone to variations among different observers. Employing machine learning to automate the diagnosis of breast cancer presents a viable option, striving to improve both precision and speed. Previous studies have primarily focused on applying various machine learning and deep learning models for the classification of breast cancer images. These methodologies leverage convolutional neural networks (CNNs) and other advanced algorithms to differentiate between benign and malignant tumors from histopathological images. Current models, despite their potential, encounter obstacles related to generalizability, computational performance, and managing datasets with imbalances. Additionally, a significant number of these models do not possess the requisite transparency and interpretability, which are vital for medical diagnostic purposes. To address these limitations, our study introduces an advanced machine learning model based on EfficientNetV2. This model incorporates state-of-the-art techniques in image processing and neural network architecture, aiming to improve accuracy, efficiency, and robustness in classification. We employed the EfficientNetV2 model, fine-tuned for the specific task of breast cancer image classification. Our model underwent rigorous training and validation using the BreakHis dataset, which includes diverse histopathological images. Advanced data preprocessing, augmentation techniques, and a cyclical learning rate strategy were implemented to enhance model performance. The introduced model exhibited remarkable efficacy, attaining an accuracy rate of 99.68%, balanced precision and recall as indicated by a significant F1 score, and a considerable Cohen’s Kappa value. These indicators highlight the model’s proficiency in correctly categorizing histopathological images, surpassing current techniques in reliability and effectiveness. The research emphasizes improved accessibility, catering to individuals with disabilities and the elderly. By enhancing visual representation and interpretability, the proposed approach aims to make strides in inclusive medical image interpretation, ensuring equitable access to diagnostic information.https://www.frontiersin.org/articles/10.3389/fmed.2024.1373244/fullmachine learningEfficientNetV2histopathological image classificationdeep learningimage processingBreakHis dataset |
| spellingShingle | Moteeb Al Moteri T. R. Mahesh Arastu Thakur V. Vinoth Kumar Surbhi Bhatia Khan Surbhi Bhatia Khan Mohammed Alojail Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancer Frontiers in Medicine machine learning EfficientNetV2 histopathological image classification deep learning image processing BreakHis dataset |
| title | Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancer |
| title_full | Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancer |
| title_fullStr | Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancer |
| title_full_unstemmed | Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancer |
| title_short | Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancer |
| title_sort | enhancing accessibility for improved diagnosis with modified efficientnetv2 s and cyclic learning rate strategy in women with disabilities and breast cancer |
| topic | machine learning EfficientNetV2 histopathological image classification deep learning image processing BreakHis dataset |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2024.1373244/full |
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